最近对结构偏见进行了针对情感三胞胎提取（ASTE）的利用，并改善了性能。另一方面，人们认识到，明确纳入结构偏见会对效率产生负面影响，而预验证的语言模型（PLM）已经可以捕获隐式结构。因此，出现了一个自然的问题：在PLM的背景下，结构性偏见仍然是必要的吗？为了回答这个问题，我们建议通过使用适配器在PLM中整合结构偏置并使用便宜的计算相对位置结构来代替句法依赖性结构来解决效率问题。基准评估是在Semeval数据集上进行的。结果表明，我们提出的结构适配器对PLM有益，并在一系列强大的基准范围内实现最先进的性能，但具有光参数需求和延迟较低。同时，我们引起了人们的担忧，即当前的评估默认值为小规模的数据不足。因此，我们为ASTE发布了一个大型数据集。新数据集的结果暗示，结构适配器在大规模上自信地有效和有效。总体而言，我们得出一个结论，即即使使用PLM，结构偏见仍然是必要的。

方面情感三胞胎提取（ASTE）旨在提取方面，意见及其情感关系作为情感三胞胎的跨度。现有的作品通常将跨度检测作为1D令牌标记问题制定，并使用令牌对的2D标记矩阵对情感识别进行建模。此外，通过利用诸如伯特（Bert）之类的审计语言编码器（PLES）的代表形式，它们可以实现更好的性能。但是，他们只是利用将功能提取器作为提取器来构建其模块，但从未深入了解特定知识所包含的内容。在本文中，我们争辩说，与其进一步设计模块以捕获ASTE的电感偏见，不如包含“足够”的“足够”功能，用于1D和2D标记：（1）令牌表示包含令牌本身的上下文含义，因此此级别，因此此级别功能带有必要的信息以进行1D标记。 （2）不同PLE层的注意力矩阵可以进一步捕获令牌对中存在的多层次语言知识，从而使2D标记受益。 （3）此外，对于简单的转换，这两个功能也可以很容易地转换为2D标记矩阵和1D标记序列。这将进一步提高标签结果。通过这样做，PLE可以是自然的标记框架并实现新的最新状态，通过广泛的实验和深入分析来验证。

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks are introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their corresponding solutions is still lacking, which we aim to fill in this survey. More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks. From the perspective of solutions, we summarize the utilization of pre-trained language models for ABSA, which improved the performance of ABSA to a new stage. Besides, techniques for building more practical ABSA systems in cross-domain/lingual scenarios are discussed. Finally, we review some emerging topics and discuss some open challenges to outlook potential future directions of ABSA.

基于方面的情绪分析（ABSA）任务由三个典型的子特点组成：术语术语提取，意见术语提取和情感极性分类。这三个子组织通常是共同执行的，以节省资源并减少管道中的错误传播。但是，大多数现有联合模型只关注编码器共享的福利在子任务之间共享，但忽略差异。因此，我们提出了一个关节ABSA模型，它不仅享有编码器共享的好处，而且还专注于提高模型效率的差异。详细地，我们介绍了双编码器设计，其中一对编码器特别侧重于候选方识对分类，并且原始编码器对序列标记进行注意。经验结果表明，我们的拟议模型显示了鲁棒性，并显着优于前一个基准数据集的先前最先进。

基于方面的情感分析（ABSA）旨在预测对给定方面表达的情感极性（SC）或提取意见跨度（OE）。 ABSA的先前工作主要依赖于相当复杂的特定方面特征诱导。最近，审计的语言模型（PLM），例如伯特（Bert）已被用作上下文建模层，以简化特征感应结构并实现最新性能。但是，这种基于PLM的上下文建模可能不是特定于方面的。因此，一个关键问题的探索还不足：如何通过PLM更好地建模特定方面的上下文？为了回答这个问题，我们试图以非侵入性的方式通过PLM增强特定方面的上下文建模。我们提出了三个特定于方面的输入转换，即伴侣，方面提示和方面标记。通过这些转变，可以实现非侵入性方面的PLM，以促进PLM，以便更多地关注句子中特定方面的环境。此外，我们为ABSA（ADVABSA）制定了对抗性基准，以查看特定于方面的建模如何影响模型的鲁棒性。 SC和OE的标准和对抗性基准的广泛实验结果证明了该方法的有效性和鲁棒性，从而在OE上产生了新的最新性能和SC上的竞争性能。

Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research, aiming to extract triplets of the aspect term, its corresponding opinion term, and its associated sentiment polarity from a given sentence. Recently, many neural networks based models with different tagging schemes have been proposed, but almost all of them have their limitations: heavily relying on 1) prior assumption that each word is only associated with a single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level interactions and treating each opinion/aspect as a set of independent words. Hence, they perform poorly on the complex ASTE task, such as a word associated with multiple roles or an aspect/opinion term with multiple words. Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously. To this end, this paper formulates the ASTE task as a multi-class span classification problem. Specifically, STAGE generates more accurate aspect sentiment triplet extractions via exploring span-level information and constraints, which consists of two components, namely, span tagging scheme and greedy inference strategy. The former tag all possible candidate spans based on a newly-defined tagging set. The latter retrieves the aspect/opinion term with the maximum length from the candidate sentiment snippet to output sentiment triplets. Furthermore, we propose a simple but effective model based on the STAGE, which outperforms the state-of-the-arts by a large margin on four widely-used datasets. Moreover, our STAGE can be easily generalized to other pair/triplet extraction tasks, which also demonstrates the superiority of the proposed scheme STAGE.

方面情绪三重态提取（ASTE）旨在从句子中提取三胞胎，包括目标实体，相关情感极性，以及合理化极性的意见跨度。现有方法缺乏目标 - 意见对之间的构建相关性，并忽略不同情绪三联体之间的相互干扰。为了解决这些问题，我们利用了两阶段框架来增强目标和意见之间的相关性：在阶段，通过序列标记提取目标和意见;然后，我们附加了一组名为可感知对的人工标签，其指示特定目标意义元组的跨度，输入句子以获得更接近相关的目标意见对表示。同时，我们通过限制令牌的注意力领域来降低三态层之间的负干扰。最后，根据可感知对的表示来识别极性。我们对四个数据集进行实验，实验结果表明了我们模型的有效性。

The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. In this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the sentiment quadruple of target-aspect-opinion-sentiment in a dialogue. DiaASQ bridges the gap between fine-grained sentiment analysis and conversational opinion mining. We manually construct a large-scale, high-quality Chinese dataset and also obtain the English version dataset via manual translation. We deliberately propose a neural model to benchmark the task. It advances in effectively performing end-to-end quadruple prediction and manages to incorporate rich dialogue-specific and discourse feature representations for better cross-utterance quadruple extraction. We finally point out several potential future works to facilitate the follow-up research of this new task. The DiaASQ data is open at https://github.com/unikcc/DiaASQ

Aspect sentiment triplet extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences. Since the initial datasets used to evaluate models on ASTE had flaws, several studies later corrected the initial datasets and released new versions of the datasets independently. As a result, different studies select different versions of datasets to evaluate their methods, which makes ASTE-related works hard to follow. In this paper, we analyze the relation between different versions of datasets and suggest that the entire-space version should be used for ASTE. Besides the sentences containing triplets and the triplets in the sentences, the entire-space version additionally includes the sentences without triplets and the aspect terms which do not belong to any triplets. Hence, the entire-space version is consistent with real-world scenarios and evaluating models on the entire-space version can better reflect the models' performance in real-world scenarios. In addition, experimental results show that evaluating models on non-entire-space datasets inflates the performance of existing models and models trained on the entire-space version can obtain better performance.

Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task that aims to extract triplets of aspect terms, sentiments, and opinion terms from review sentences. Recently, span-level models achieve gratifying results on ASTE task by taking advantage of the predictions of all possible spans. Since all possible spans significantly increases the number of potential aspect and opinion candidates, it is crucial and challenging to efficiently extract the triplet elements among them. In this paper, we present a span-level bidirectional network which utilizes all possible spans as input and extracts triplets from spans bidirectionally. Specifically, we devise both the aspect decoder and opinion decoder to decode the span representations and extract triples from aspect-to-opinion and opinion-to-aspect directions. With these two decoders complementing with each other, the whole network can extract triplets from spans more comprehensively. Moreover, considering that mutual exclusion cannot be guaranteed between the spans, we design a similar span separation loss to facilitate the downstream task of distinguishing the correct span by expanding the KL divergence of similar spans during the training process; in the inference process, we adopt an inference strategy to remove conflicting triplets from the results base on their confidence scores. Experimental results show that our framework not only significantly outperforms state-of-the-art methods, but achieves better performance in predicting triplets with multi-token entities and extracting triplets in sentences contain multi-triplets.

方面情绪三重态提取（Aste）旨在识别目标，他们的情感极化和意见解释句子的情绪。 Aste可以自然地分为3个原子子组织，即目标检测，意见检测和情绪分类。我们认为针对目标 - 意见对的合适的子任务组合，组成特征提取，以及子任务之间的互动将是成功的关键。然而，由于缺陷的子任务制定，子最优特征表示或缺少子任务相互作用，在“一对多”或“多对一”的情况下可能导致不存在的情绪三体，或导出不存在的情绪三元组。在本文中，我们将Aste划分为目标 - 意见联合检测和情绪分类子任务，这与人类认知符合，并且相应地利用序列编码器和表编码器来处理它们。表编码器在令牌对等级提取情绪，从而可以容易地捕获目标和意见之间的组成特征。要在子任务之间建立显式交互，我们利用表格表示来指导序列编码，并将序列功能注入到表编码器中。实验表明，我们的模型在六个受欢迎的ASTE数据集中优于最先进的方法。

最近的知名作品表明，基于方面的情感分类（ABSC）令人鼓舞，而隐性方面情感建模仍然是必须解决的问题。我们的初步研究表明，隐式方面的情感通常取决于相邻方面的情感，这表明我们可以通过局部情感依赖性建模提取隐式情感。我们根据经验情感模式（SP）制定了局部情感聚合范式（LSA），以解决情感依赖性建模。与现有方法相比，LSA是一种有效的方法，它可以在局部情感聚合窗口中学习隐性情感，该窗口解决了效率问题并避免了基于语法的方法的令牌节点对齐问题。此外，我们根据梯度下降来完善一种差分加权方法，该方法指导了情感聚合窗口的构建。根据实验结果，LSA对所有客观的ABSC模型都有有效，可以在三个公共数据集上获得最先进的性能。 LSA是一种自适应范式，准备适应现有模型，我们发布代码以提供洞察力以改善现有的ABSC模型。

具有数百万参数的基于变压器的预训练模型需要大量存储。最近的方法通过培训适配器解决了这一缺点，但是这些方法仍然需要相对较大的参数。在这项研究中，提出了一种令人惊讶的简单但有效的适配器体系结构的Adapterbias。AdapterBias向变压器层的隐藏输出添加了代币依赖性转移，以适应仅使用向量和线性层的下游任务。进行了广泛的实验，以证明适配性的有效性。实验表明，与先前的作品相比，我们提出的方法可以大大减少可训练的参数，而任务性能与微调的预训练模型相比最小。我们进一步发现，适应性比亚斯自动学习以将更重要的表示形式分配给与任务相关的代币转移。

及时调整是以参数有效的方式对预训练的预训练语言模型的新范式。在这里，我们探讨了超级核武器的使用来产生超预价：我们提出了HyperPrompt，这是一种用于迅速基于变形金刚自我注意的任务调节的新型体系结构。超预要是通过超网络通过一代人来学习的端到端。 HyperPrompt允许网络学习特定于任务的功能地图，其中超预告是要参与的查询的任务全局记忆，同时启用了任务之间的灵活信息共享。我们表明，HyperPrompt与强大的多任务学习基线具有竞争力，其额外的任务条件参数的$ 0.14 \％$ $ \％，实现了出色的参数和计算效率。通过广泛的经验实验，我们证明，超级启示可以比强大的T5多任务学习基准和参数效率高效的适配器变体获得卓越的性能，包括及时调整和SuplyFormer ++在许多模型尺寸的自然语言理解胶水和SuperGrue的基准上。

基于多模式方面的情感分类（MABSC）是一项新兴的分类任务，旨在将给定目标的情感分类，例如具有不同模式的数据中提到的实体。在带有文本和图像的典型多模式数据中，以前的方法不能充分利用图像的细颗粒语义，尤其是与文本的语义结合在一起，并且不完全考虑对细粒图像之间的关系进行建模信息和目标，这导致图像的使用不足和不足以识别细粒度的方面和意见。为了应对这些局限性，我们提出了一个新的框架SEQCSG，包括一种构建顺序跨模式语义图和编码器模型的方法。具体而言，我们从原始图像，图像标题和场景图中提取细粒度的信息，并将它们视为跨模式语义图的元素以及文本的令牌。跨模式语义图表示为具有多模式可见矩阵的序列，指示元素之间的关系。为了有效地利用跨模式语义图，我们建议使用目标提示模板的编码器解码器方法。实验结果表明，我们的方法优于现有方法，并在两个标准数据集MABSC上实现了最新方法。进一步的分析证明了每个组件的有效性，我们的模型可以隐含地学习图像的目标和细粒度信息之间的相关性。

基于方面的情感分析（ABSA）是一项精细的情感分析任务，它的重点是检测句子中的情感极性。但是，它始终对多方面的挑战敏感，在句子中，多个方面的特征将相互影响。为了减轻此问题，我们设计了一个新颖的培训框架，称为对比度跨通道数据增强（C3 DA），该框架利用了一个内域的发电机来构建更多的多种相应样本，然后通过对比度模型通过对比度学习的稳健性，从而通过对比度学习的稳健性这些生成的数据。实际上，鉴于生成预审预测的语言模型和一些有限的ABSA标记数据，我们首先采用一些参数效率的方法来执行内域微调。然后，所获得的内域发生器用于从两个通道（即方面增强通道和极性增强通道）生成合成句子，该句子分别在给定的方面和极性上生成句子条件。具体而言，我们的C3 DA以跨渠道的方式执行句子生成以获取更多句子，并提出了熵最小化过滤器以滤除低质量生成的样品。广泛的实验表明，我们的C3 DA可以在准确性和宏观上胜过约1％的基准，而不会增加1％。代码和数据在https://github.com/wangbing1416/c3da中发布。

Pre-trained Language Models (PLMs) which are trained on large text corpus through the self-supervised learning method, have yielded promising performance on various tasks in Natural Language Processing (NLP). However, though PLMs with huge parameters can effectively possess rich knowledge learned from massive training text and benefit downstream tasks at the fine-tuning stage, they still have some limitations such as poor reasoning ability due to the lack of external knowledge. Incorporating knowledge into PLMs has been tried to tackle these issues. In this paper, we present a comprehensive review of Knowledge-Enhanced Pre-trained Language Models (KE-PLMs) to provide a clear insight into this thriving field. We introduce appropriate taxonomies respectively for Natural Language Understanding (NLU) and Natural Language Generation (NLG) to highlight the focus of these two kinds of tasks. For NLU, we take several types of knowledge into account and divide them into four categories: linguistic knowledge, text knowledge, knowledge graph (KG), and rule knowledge. The KE-PLMs for NLG are categorized into KG-based and retrieval-based methods. Finally, we point out some promising future directions of KE-PLMs.

Simile recognition involves two subtasks: simile sentence classification that discriminates whether a sentence contains simile, and simile component extraction that locates the corresponding objects (i.e., tenors and vehicles). Recent work ignores features other than surface strings. In this paper, we explore expressive features for this task to achieve more effective data utilization. Particularly, we study two types of features: 1) input-side features that include POS tags, dependency trees and word definitions, and 2) decoding features that capture the interdependence among various decoding decisions. We further construct a model named HGSR, which merges the input-side features as a heterogeneous graph and leverages decoding features via distillation. Experiments show that HGSR significantly outperforms the current state-of-the-art systems and carefully designed baselines, verifying the effectiveness of introduced features. Our code is available at https://github.com/DeepLearnXMU/HGSR.

面向目标的意见单词提取（TOWE）是一项精细的情感分析任务，旨在从句子中提取给定意见目标的相应意见单词。最近，深度学习方法在这项任务上取得了显着进步。然而，由于昂贵的数据注释过程，TOWE任务仍然遭受培训数据的稀缺性。有限的标记数据增加了测试数据和培训数据之间分配变化的风险。在本文中，我们建议利用大量未标记的数据来通过增加模型对变化分布变化的暴露来降低风险。具体而言，我们提出了一种新型的多透明一致性正则化（MGCR）方法，以利用未标记的数据并设计两个专门用于TOWE的过滤器，以在不同的粒度上过滤嘈杂的数据。四个TOWE基准数据集的广泛实验结果表明，与当前的最新方法相比，MGCR的优越性。深入分析还证明了不同粒度过滤器的有效性。我们的代码可在https://github.com/towessl/towessl上找到。

基于方面的情绪分析旨在确定产品评论中特定方面的情感极性。我们注意到，大约30％的评论不包含明显的观点词，但仍然可以传达清晰的人类感知情绪取向，称为隐含情绪。然而，最近的基于神经网络的方法几乎没有关注隐性情绪，这一审查有所关注。为了克服这个问题，我们通过域名语言资源检索的大规模情绪注释的Corpora采用监督对比培训。通过将隐式情感表达式的表示对准与具有相同情绪标签的人，预培训过程可以更好地捕获隐含和明确的情绪方向，以便在评论中的方面。实验结果表明，我们的方法在Semeval2014基准上实现了最先进的性能，综合分析验证了其对学习隐含情绪的有效性。